蚂蚁金服蚂蚁集团-AI Feedback Loop 工程师-杭州/上海
任职要求
1.计算机基础: 3年以上数据研发或后端开发经验,精通 Java/Scala/Python 其中一种,代码风格优雅。 2.大数据栈: 熟练掌握 Flink, Spark, Kafka, HBase 等大数据生态组件,有处理PB 级海量数据…
工作职责
1. 建设高实时性的用户反馈数据流: 负责构建大规模,高并发的实时数据链路,处理海量用户交互日志。 负责对多模态的用户反馈信号进行全链路的清洗,归因与结构化处理,为模型迭代提供标准化的数据养料。 2. 打造自动化的大模型演进闭环 建设面向RLHF和DPO的数据工程体系,自动化构建高质量的偏好数据集,并制成模型的持续对齐与优化。 设计Bad Case自动发现与诊断机制,通过数据驱动的方式识别模型与知识短板,协同算法与知识工程团队形成质量提升的闭环,持续降低模型幻觉率。 3. 质量归因: 解决多轮交互场景下的归因难题:通过因果推断等技术手段,精准定位用户体验波动的核心因子,知道算法迭代方向。
1. 建设高实时性的用户反馈数据流: 负责构建大规模,高并发的实时数据链路,处理海量用户交互日志。 负责对多模态的用户反馈信号进行全链路的清洗,归因与结构化处理,为模型迭代提供标准化的数据养料。 2. 打造自动化的大模型演进闭环 建设面向RLHF和DPO的数据工程体系,自动化构建高质量的偏好数据集,并制成模型的持续对齐与优化。 设计Bad Case自动发现与诊断机制,通过数据驱动的方式识别模型与知识短板,协同算法与知识工程团队形成质量提升的闭环,持续降低模型幻觉率。 3. 质量归因: 解决多轮交互场景下的归因难题:通过因果推断等技术手段,精准定位用户体验波动的核心因子,知道算法迭代方向。

Key Responsibilities Onboarding and training: Guide new clients through setup, ensuring smooth adoption and confidence in using Evelab Insight tools. Relationship management: Build strong, long-term partnerships with clients, acting as their advocate within Evelab Insight. Pain Point Identification: Identify client pain points, design and develop tailored solutions that maximize efficiency and ROI. Solution optimization: Track client usage and outcomes, proactively addressing risks and surfacing insights. Upsell and expansion: Spot opportunities to introduce new features or services aligned with client goals. Cross-functional collaboration: Partner with Sales, Product, and Support teams to deliver seamless client experiences. Feedback loop: Capture client insights to inform product development and service improvements. Required

● Lead the FDE team of Alibaba Cloud International Business to partner with key customers to design, co-develop, debug and deploy vertical agentic solutions that generate measurable impact. ● Provide technical guidance on model tuning, agent workflows, CI/CD pipelines, and production readiness, ensuring rapid iteration from prototype to robust, scalable systems. ● Drive the evolution of the Agent Development Platform, working with internal engineer teams to create reusable tools, documentation, and accelerators, enabling developers to easily build, test and deploy agents. ● Systematize field insights from customer engagements and share learnings with product teams to improve future experiences and capabilities. ● Establish a structured feedback loop with product and engineering teams, synthesizing field insights from customer engagements to improve future experiences and capabilities. ● Elevate engineering excellence across the FDE team by establishing code standards, architectural best practices and benchmarks.